With the demand for human donor organs desperately outstripping supply, scientists are working to see if genetically edited pig organs can bridge the gap. Leonardo Riella, medical director for kidney transplantation at Massachusetts General Hospital, checks on Tim Andrews after his pig kidney transplant. Surgeons at Massachusetts General Hospital have removed a genetically engineered pig kidney from a 67-year-old New Hampshire man after a period of decreasing kidney function, the hospital confirmed to WIRED in a statement. The organ functioned for nearly nine months, longer than previous pig organ transplants, before it was removed on October 23. Tim Andrews received the pig kidney on January 25 after being on dialysis for more than two years due to end-stage kidney disease.

Argumentation frameworks (AFs) are a foundational tool in artificial intelligence for modeling structured reasoning and conflict. SCC-recursiveness is a well-known design principle in which the evaluation of arguments is decomposed according to the strongly connected components (SCCs) of the attack graph, proceeding recursively from "higher" to "lower" components. While SCC-recursive semantics such as \cft and \stgt have proven effective for finite AFs, Baumann and Spanring showed the failure of SCC-recursive semantics to generalize reliably to infinite AFs due to issues with well-foundedness. We propose two approaches to extending SCC-recursiveness to the infinite setting. We systematically evaluate these semantics using Baroni and Giacomin's established criteria, showing in particular that directionality fails in general. We then examine these semantics' behavior in finitary frameworks, where we find some of our semantics satisfy directionality. These results advance the theory of infinite argumentation and lay the groundwork for reasoning systems capable of handling unbounded or evolving domains.

Dialectical systems are a mathematical formalism for modeling an agent updating a knowledge base seeking consistency. Introduced in the 1970s by Roberto Magari, they were originally conceived to capture how a working mathematician or a research community refines beliefs in the pursuit of truth. Dialectical systems also serve as natural models for the belief change of an automated agent, offering a unifying, computable framework for dynamic belief management. The literature distinguishes three main models of dialectical systems: (d-)dialectical systems based on revising beliefs when they are seen to be inconsistent, p-dialectical systems based on revising beliefs based on finding a counterexample, and q-dialectical systems which can do both. We answer an open problem in the literature by proving that q-dialectical systems are strictly more powerful than p-dialectical systems, which are themselves known to be strictly stronger than (d-)dialectical systems. This result highlights the complementary roles of counterexample and contradiction in automated belief revision, and thus also in the reasoning processes of mathematicians and research communities.

In medical settings, it is critical that all who are in need of care are correctly heard and understood. When this is not the case due to prejudices a listener has, the speaker is experiencing \emph{testimonial injustice}, which, building upon recent work, we quantify by the presence of several categories of unjust vocabulary in medical notes. In this paper, we use FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization (e.g., age, gender, and race) by way of contributing to testimonial injustice. To achieve this, we review physicians' notes for each patient, where we identify occurrences of unjust vocabulary, along with the demographic features present, and use causal discovery to build a Structural Causal Model (SCM) relating those demographic features to testimonial injustice. We analyze and discuss the resulting SCMs to show the interaction of these factors and how they influence the experience of injustice. Despite the potential presence of some confounding variables, we observe how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice. There is no single root of injustice and thus intersectionality cannot be ignored. These results call for considering more than singular or equalized attributes of who a person is when analyzing and improving their experiences of bias and injustice. This work is thus a first foray at using causal discovery to understand the nuanced experiences of patients in medical settings, and its insights could be used to guide design principles throughout healthcare, to build trust and promote better patient care.

Finite mixture modelling is a popular method in the field of clustering and is beneficial largely due to its soft cluster membership probabilities. However, the most common algorithm for fitting finite mixture models, the EM algorithm, falls victim to a number of issues. We address these issues that plague clustering using finite mixture models, including convergence to solutions corresponding to local maxima and algorithm speed concerns in high dimensional cases. This is done by developing two novel algorithms that incorporate a spectral decomposition of the data matrix and a non-parametric bootstrap sampling scheme. Simulations show the validity of our algorithms and demonstrate not only their flexibility but also their ability to avoid solutions corresponding to local-maxima, when compared to other (bootstrapped) clustering algorithms for estimating finite mixture models. Our novel algorithms have a typically more consistent convergence criteria as well as a significant increase in speed over other bootstrapped algorithms that fit finite mixture models.

Back in May, Samsung Electronics hosted their first'Samsung 6G Forum' (S6GF) that I blogged about here. The talk by Prof. Jeffrey Andrews, The University of Texas at Austin, deserves its own separate post. The topic of his talk was'Deep Learning in the 6G Air Interface'. In a presentation, Andrews noted emerging 5G applications including autonomous vehicles and robots require situational awareness going beyond what they can sense alone. "Although driverless cars are built to be autonomous, they don't really work right unless they can see things and know about things outside of their own field of vision. Otherwise, they'll have to drive too slowly, too conservatively."

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The heat of July is upon us, which also means we're exactly halfway to 2023. So, it seems like a good time to pause and ask: What are the biggest AI trends so far in mid-2022? The colossal AI trend that all other AI trends serve is the increased scale of artificial intelligence in organizations, said Whit Andrews, vice president and distinguished analyst at Gartner Research. That is, more and more companies are entering an era where AI is an aspect of every new project.

In Spain, the Madrid Metro uses AI to monitor its network and reduce energy consumption by 25%. In the U.S., a beverage company uses AI to drive sales by analyzing retailers and markets. In Europe, an energy company trains its engineers and managers in a digital twin factory powered by AI. In the Middle East, a telco's AI-powered virtual assistant speaks to 1.65 million customers every month in different Arab dialects and English. Undoubtedly, AI is in full adoption around the world, with all industries recognizing it as the next big thing in tech.

Sirona Medical, a company developing an "operating system" for digital radiology, has acquired Nines– a company that has developed FDA-cleared analysis and triage algorithms. This acquisition comes during a somewhat shaky moment in AI-based radiology. But Sirona is betting this move proves out its thesis: to bring AI into the clinical workflow, we need to rebuild things from the ground up. To understand where Sirona and Nines fit together, think about the IT behind radiology as a layer cake. The first layer of that cake is made of medical image databases.

Issues around ethical AI have garnered more attention over the past several years. Tech giants from Facebook to Google to Microsoft have already established and published principles to demonstrate to stakeholders -- customers, employees, and investors -- that they understand the importance of ethical or responsible AI. So it was a bit of a black eye last year when the co-head of Google's Ethical AI group, Timnit Gebru, was fired following a dispute with management over a scholarly paper she coauthored and was scheduled to deliver at a conference. Now Gebru has established her own startup focused on ethical AI. The Distributed Artificial Intelligence Research Institute (DAIR) produces interdisciplinary AI research, according to the organization.